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Process Chemometrics in the

Dow Chemical company

Zdravko Stefanov and Leo Chiang

Analytical Technology Center

The Dow Chemical Company

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OutlineOutline

• Who we are and how we approach

problems

• Process chemometrics for batch

processes

• Process chemometrics for continuous

processes

• Online multivariate monitoring

• Summary

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Who we areWho we are

and problem approachand problem approach

• The chemometrics group:

Ivan Castillo, Leo Chiang, Swee-Teng Chin, Kedar Dave, Eldad Herceg, Bryant LaFreniere,

Randy Pell, Mary Beth Seasholtz, Zdravko (Z) Stefanov

• We are part of the Analytical Technology Center at Dow

• We are serious contributors to a fundamental approach to solve plant problems based on hypothesis testing and hard data

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Process chemometrics forProcess chemometrics for

batch processes batch processes –– example 1example 1

• High Impurity Level: Big Problem!

• One week to fix the problem before loss of the entire campaign

• Multimillion USD of profit margin at risk

• Process

• System of 2 batch reactors in series

• Reactor sequences are multistep with complicated solid/2 liquid phases chemistry

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• The approach – batch-unfolded PLS

• X – process data from all steps

• Y – the impurity concentration for each batch

• R1 and R2 data were combined into one matrix

Y

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Number of

unfolded

variables

Number of principal

components

R2X R2Y Q2Y

77 2 0.733 0.824 0.746

0.0

1.0

2.0

3.0

4.0

-0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

YV

ar(

%C

I (<

0.4

%))

YPred[2](%CI (< 0.4%))

y=1*x-3.893e-008R2=0.8236

SIMCA-P+ 12.0.1 - 2010-09-12 16:28:44 (UTC-6)

• Important variables • R2 agitator amps during

step 34 • R2 weight during step 34

• The first two variables reflect the amount of work put by the agitator in R2, respectively the mixing quality. • The batches were made “skinnier”, i.e. the reactant amounts

were reduced by 10% compared to the original recipe. This was done in order to improve the mixing.

• After these changes were implemented, the plant resumed operation and all consecutive batches of the product were produced within specification.

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• Problem with too many batches out of specification

• Poor control of reaction extent

• The reactors are not trajectory controlled, but recipe controlled

• Large variability in quality

• Two trains in parallel, experience similar problem

• The process is complex fed-batch with two stages and multiple steps (35 in two stages)

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• The approach – batch-unfolded PLS

• X – process data from all steps

• Y – the reaction extent

• The two reactors are modeled separately

Y

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Calibration Validation

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• High (red) and low (blue) reaction extent batches

• Mass and energy balance related variables show large differences

• Significant oscillations are also observed

• Control system changes were implemented

• Quality was improved to the effect of multimillion USD

Step 214 Step 214

Step 214 Step 221

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continuous processescontinuous processes

• For a continuous plant, a product stream was sampled every 3 hours for product quality control purpose

• The plant proposed to reduce sample frequency to once every 6 hours (i.e. grab 4 samples per day) to reduce cost and minimize safety risk

• However their customer rejected this proposal because of higher quality risk. A single event of accepting offgrade material (i.e., type-II error) can lead to a huge loss.

• Question: Can we come up with a solution that will satisfy both the plant and the customer?

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• Solution – inferential sensor • Implement inferential sensor as a conditional sampling

solution • If key process conditions (e.g., temperature, reflux flow,

feed flow, etc.) predict that product quality will be unusual (or out of spec), then we can sample more often (i.e., every 3 hr) to confirm

Process inputs

(Temp, Pressure)

Inferential Sensor;

y = f(x)

Every 3 hr

Every minute

Result

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• Training data contain 60 variables for 3 months

• PLS model uses 10 variables

• Model quality is good (R2 = 0.93; RMSE error = 0.74 ppm)

• The model is implemented on-line

Measured quality

Predicted quality

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• Good online performance

• We have implemented a real-time diagnostics tool (i.e., T2 and Q statistics) to monitor model performance

Specification = 10 ppm

Trigger limit = 6.8 ppm

Measured quality

Predicted quality

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• If predicted quality > 6.8 ppm, this triggers use of Critical Quality Control procedure.

• Critical procedure calls for increased sampling frequency (3 hours between samples).

• This continues until 2 consecutive results are within control limits.

• If predicted quality > 8.5 ppm, the plant will follow the above critical procedure and also divert product to a offgrade tank

Statistical Quality Control

Operating Discipline

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• An inferential model was implemented in real time since April 2011 and proved to be accurate and effective.

• The customer approves a new Operating Discipline that uses model prediction to infer sampling frequency.

• The plant now samples at a rate of every 6 hours unless the model prediction is above the critical limit.

• This is the first ever inferential sensor application for sample size optimization in Dow.

Summary

Now, both the plant and the customer are happy!

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Multivariate monitoringMultivariate monitoring

• The plant from batch example 1 loved the technology!

• So we installed online multivariate monitoring software on R1 and R2

• Caught a multitude of other issues not known before, such as

• Catalyst tank failed to stir before loading due to control system limit being set wrong

• Vibrations from a steam tracing line distorting a weigh cell and therefore the recipe was not loaded properly

• And others

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• The classic multivariate analysis has proven to be very useful for troubleshooting of plant problems, but • It is reactive – it is applied only after the problems

are well developed • Leads to missed opportunity for even more

savings • Online multivariate analysis / monitoring / fault

detection is real time • It is proactive • Will detect the onset of the problems early before

they lead to large losses • We are looking for a commercial vendor but until we

find it • In-house tool based on Excel now can perform online

monitoring for batch and continuous processes

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• Supported features

• Batch and continuous processes

• Multiple products/recipes/operating conditions

• Automatic/manual model selection

• Aspen IP.21 and legacy DOW GPI DAP data sources

• The user can save the configuration for easy reloading

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The T2 and DModX control charts

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If a point in the control charts is clicked, a contribution

chart will appear

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If a bar on the contribution chart is clicked,

it will plot a trend of the actual variable

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SummarySummary

• Process chemometrics has been very successful at Dow Chemical

• The process chemometrics group is steadily growing, however

• Recent searches for European hire were not successful

• We are looking to collaborate with universities to help develop students that can be successful chemometricians at Dow.

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QuestionsQuestions

Thank you for your attention.

Questions?

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